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Proof of Proposition 7, p. 29 — Δ_H < Λ_{H1}

Proved
CustomerVoice.Thresholds.DeltaH_lt_LambdaH1

by mikedeng1 · Oct 15, 2026 · Mathlib 0df444a (Lean v4.33.1)

inequalityp2o-batch-pfp2ap2o-gran-per-chapterp2o-plan-paperp2o-v1threshold

Let (θH,θL,α,γ,k,s)(\theta_H,\theta_L,\alpha,\gamma,k,s)(θH​,θL​,α,γ,k,s) satisfy the standing assumptions θH>θL>0\theta_H>\theta_L>0θH​>θL​>0, α,γ∈(0,1)\alpha,\gamma\in(0,1)α,γ∈(0,1), k>0k>0k>0, s>0s>0s>0. Then the high type's A-Learning threshold is strictly smaller than the first high-type O-Learning threshold:

ΔH=γ[2s+(1+γ)k](1−γ)(2s+γk)<ΛH1=γ(s+k)(1−γ)s.\Delta_H=\frac{\gamma[2s+(1+\gamma)k]}{(1-\gamma)(2s+\gamma k)}<\Lambda_{H1}=\frac{\gamma(s+k)}{(1-\gamma)s}.ΔH​=(1−γ)(2s+γk)γ[2s+(1+γ)k]​<ΛH1​=(1−γ)sγ(s+k)​.

This is the first of the three comparisons in the proof of Proposition 7: the high type's incentive to tell the truth binds at a lower ratio θH/θL\theta_H/\theta_LθH​/θL​ under A-Learning than under O-Learning when the seller-acceptance constraint binds.

Preamble
import Mathlib
import Definitions.Def_CustomerVoice_Thresholds_Setting
Formal statement
namespace CustomerVoice.Thresholds

theorem DeltaH_lt_LambdaH1 (M : Model) (hM : M.Standing) :
    M.DeltaH < M.LambdaH1 := by sorry

end CustomerVoice.Thresholds
Source
Guo, The Voice of Customers in Customization, SSRN working paper 3994319 (version of 2023-11-29, https://ssrn.com/abstract=3994319), p. 29, Appendix, Proof of Proposition 7
Human review
  • Endorsed by Shuze Chen · Oct 16, 2026

    Confirmed by the moderator at approval.

  • Endorsed by mikedeng1 · Oct 16, 2026

    Confirmed by the mission captain (proposal self-audit).

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